- Full Description
This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, and natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.
- Table of Contents
Table of Contents
- An Introduction to Logic.
- An Introduction to Learning and Search.
- Representations for Mining and Learning.
- Generality and Logical Entailment.
- The Upgrading Story.
- Inducing Theories.
- Probabilistic Logic Learning.
- Kernels and Distances for Structured Data.
- Computational Aspects of Logical and Relational Learning.
- Author Index.
- Subject Index.
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